Performance monitoring and fault detection in control systems

As the sophistication of systems used in chemical processing industries increases and demands for high quality products manufactured at low costs mount, the need for improved methods for automatic monitoring of processes arises. This is particularly true for systems operating under automatic control where the control system often acts to eliminate early warning signs of process changes. This thesis examines problems in the area of dynamic system monitoring, with emphasis on control systems. Problems in the areas of controller performance monitoring, estimation, and fault detection are considered.; In the area of controller performance monitoring, techniques for assessing performance in a minimum variance framework are developed which can address unstable and nonminimum-phase plants and systems with unstable controllers. An alternative approach to evaluating deterioration in performance of control systems is formulated using a likelood ratio testing.; The problem of constrained state estimation is pursued using Moving Horizon Estimation. It is shown that previous formulations of this estimation technique can be unstable when constraints on the innovations and estimated states are included. By expanding the constraint set and modifying the estimation objective, stability is guaranteed.; Several approaches to fault detection are considered. First, the simultaneous design of linear fault detection filters and controllers is considered using the four parameter controller framework. Second, using the Moving Horizon Estimation framework, a model based fault detection scheme capable of directly incorporating a class of bounded model uncertainty is developed. The proposed method is demonstrated on a simulation example of a cold tandem steel mill. Finally, a statistical framework for general change detection problems is presented. This method uses a two-model approach, where signals and parameters subject to change are modeled by Brownian motion for the faulty case and by constant values in the nominal case.; The use of qualitative modeling in detection and control problems is formulated using propositional logic. Symptom aided detection and multiobjective performance prioritization are among the problems which can be solved in this framework using mixed integer linear and quadratic programming.